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Documentation Index

Fetch the complete documentation index at: https://docs.langchain.com/llms.txt

Use this file to discover all available pages before exploring further.

This page covers all LangChain integrations with Oracle Cloud Infrastructure (OCI).

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Installation and Setup

pip install langchain-oci oci

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Authentication

Initializing ChatOCIGenAI is resource-intensive. For optimal performance, it is recommended to treat this client as a singleton and reuse the instance across your application.
Four authentication methods are supported for OCI services. All methods follow the standard OCI SDK authentication.

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API Key (Default)

Uses credentials from ~/.oci/config: 
from langchain_oci import ChatOCIGenAI

llm = ChatOCIGenAI(
    model_id="meta.llama-3.3-70b-instruct",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..xxx",
    auth_type="API_KEY",        # Default
    auth_profile="DEFAULT",      # Profile name in ~/.oci/config
)

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Security Token

For session-based authentication: 
oci session authenticate --profile-name MY_PROFILE

llm = ChatOCIGenAI(
    model_id="meta.llama-3.3-70b-instruct",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..your-compartment-id",
    auth_type="SECURITY_TOKEN",
    auth_profile="MY_PROFILE",
)

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Instance Principal

For applications running on OCI compute instances: 
llm = ChatOCIGenAI(
    model_id="meta.llama-3.3-70b-instruct",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..your-compartment-id",
    auth_type="INSTANCE_PRINCIPAL",
)

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Resource Principal

For OCI Functions and other resources: 
llm = ChatOCIGenAI(
    model_id="meta.llama-3.3-70b-instruct",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..your-compartment-id",
    auth_type="RESOURCE_PRINCIPAL",
)

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OCI Generative AI

Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which are available through a single API. Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters.

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Chat Models

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ChatOCIGenAI

Main chat model for OCI Generative AI service with full LangChain feature support. See usage example. 
from langchain_oci import ChatOCIGenAI

llm = ChatOCIGenAI(
    model_id="meta.llama-3.3-70b-instruct",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..xxx",
)
Supported Features:
  • ✅ Tool calling (including parallel tools with Llama 4+)
  • ✅ Structured output (Pydantic, JSON mode)
  • ✅ Vision & multimodal (13+ models support images)
  • ✅ Streaming (sync and async)
  • ✅ Async operations (ainvoke, astream, abatch)
  • ✅ PDF, video, audio processing (Gemini models)

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ChatOCIOpenAI

OpenAI Responses API compatibility for OCI commercial OpenAI models. 
from langchain_oci import ChatOCIOpenAI

llm = ChatOCIOpenAI(
    model="openai.gpt-5.4",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..your-compartment-id",
)
Features:
  • OpenAI-compatible interface
  • Conversation store support for persistent memory
  • Access to GPT-4, GPT-5, o1, o3 models (where available)

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Embedding Models

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OCIGenAIEmbeddings

Text and image embedding models. See usage example. 
from langchain_oci import OCIGenAIEmbeddings

# Text embeddings
embeddings = OCIGenAIEmbeddings(
    model_id="cohere.embed-english-v3.0",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..xxx",
)

# Image embeddings (with multimodal models)
vector = embeddings.embed_image("./architecture_diagram.png")
Available Models:
  • cohere.embed-english-v3.0 (1024 dimensions)
  • cohere.embed-multilingual-v3.0 (1024 dimensions)
  • cohere.embed-v4.0 (text + image, 256-1536 dimensions)

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Vision & Multimodal

13+ vision-capable models across Meta Llama, Google Gemini, xAI Grok, and Cohere: 
from langchain.messages import HumanMessage
from langchain_oci import ChatOCIGenAI, load_image

llm = ChatOCIGenAI(
    model_id="meta.llama-3.2-90b-vision-instruct",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..xxx",
)

message = HumanMessage(content=[
    {"type": "text", "text": "Identify all microservices, data flows, and external dependencies."},
    load_image("./architecture_diagram.png"),
])
response = llm.invoke([message])
Gemini Multimodal (PDF, video, audio): 
import base64
from langchain.messages import HumanMessage
from langchain_oci import ChatOCIGenAI

llm = ChatOCIGenAI(
    model_id="google.gemini-2.5-flash",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.compartment.oc1..your-compartment-id",
)

# PDF processing - Extract structured data from contracts
with open("vendor_contract.pdf", "rb") as f:
    pdf_data = base64.b64encode(f.read()).decode()

message = HumanMessage(content=[
    {"type": "text", "text": "Extract: parties, effective date, payment terms. Return as JSON."},
    {"type": "document_url", "document_url": {"url": f"data:application/pdf;base64,{pdf_data}"}}
])
response = llm.invoke([message])

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AI Agents

Create LangGraph-powered ReAct agents with OCI models: 
from langchain.tools import tool
from langchain_oci import create_oci_agent

@tool
def query_infrastructure(resource_type: str, region: str) -> dict:
    """Query OCI infrastructure status and health metrics.

    Args:
        resource_type: Type of resource (compute, database, network)
        region: OCI region to query
    """
    # Example: Call OCI monitoring API
    return {
        "status": "healthy",
        "active_instances": 12,
        "cpu_utilization": "45%",
        "alerts": []
    }

agent = create_oci_agent(
    model_id="meta.llama-4-scout-17b-16e-instruct",
    tools=[query_infrastructure],
    compartment_id="ocid1.compartment.oc1..xxx",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    system_prompt="You are an infrastructure monitoring assistant.",
)

from langchain.messages import HumanMessage
result = agent.invoke({
    "messages": [HumanMessage(content="Check compute resource health in us-ashburn-1")]
})

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Provider Coverage

ProviderExample ModelsKey Features
MetaLlama 3.2, 3.3, 4 (Scout, Maverick)Vision, parallel tool calls
GoogleGemini 2.0/2.5 Flash, Flash Lite, ProPDF, video, audio processing
xAIGrok 3, 4 (Fast, Mini)Vision, reasoning modes
CohereCommand R+, Command ARAG optimization, V2 vision
OpenAIGPT-5.4, GPT-5, o1, o3Reasoning (via ChatOCIOpenAI)
Note: Model availability varies by region. See the OCI Generative AI documentation for the current model catalog.

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OCI Data Science Model Deployments

OCI Data Science is a fully managed and serverless platform for data science teams. Deploy custom models as endpoints using the OCI Data Science Model Deployment Service.

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ChatOCIModelDeployment

Chat model for OCI Data Science Model Deployments. See usage example. 
from langchain_oci import ChatOCIModelDeployment

llm = ChatOCIModelDeployment(
    endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com/ocid1.datasciencemodeldeployment.oc1.iad.xxx/predict",
    model="odsc-llm",
)

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ChatOCIModelDeploymentVLLM

Optimized for vLLM-based deployments with streaming support: 
from langchain_oci import ChatOCIModelDeploymentVLLM

llm = ChatOCIModelDeploymentVLLM(
    endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com/ocid1.datasciencemodeldeployment.oc1.iad.xxx/predict",
    model="meta-llama/Llama-2-7b-chat-hf",
    streaming=True,
)

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ChatOCIModelDeploymentTGI

For Text Generation Inference (TGI) deployments: 
from langchain_oci import ChatOCIModelDeploymentTGI

llm = ChatOCIModelDeploymentTGI(
    endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com/ocid1.datasciencemodeldeployment.oc1.iad.xxx/predict",
)

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Samples

For comprehensive guides covering all features, see the langchain-oci samples:
SampleLevelTopics
01: Getting StartedBeginnerAuthentication, basic chat, providers
02: Vision & MultimodalBeginnerImage analysis, PDF, video, audio
03: Building AI AgentsIntermediateReAct agents, tools, memory
04: Tool Calling MasteryIntermediateParallel tools, workflows
05: Structured OutputIntermediatePydantic schemas, JSON modes
07: Async for ProductionAdvancedainvoke, astream, FastAPI
09: Provider Deep DiveSpecializedMeta, Gemini, Cohere, xAI specifics
10: EmbeddingsSpecializedText & image embeddings, RAG

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Additional Resources

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